Analysis of irregularly spaced time series
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Date
2019
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Abstract
In this thesis, we propose novel stationary time series models that can be used when the observations are taken on irregularly spaced times. First, we present a model with a firstorder moving average structure, and then we generalized it to consider an autoregressive component. We called the first model irregularly spaced first-order moving average and the second one irregularly spaced first-order autoregressive moving average. Their definitions and properties are established. We present their state-space representations and their one-step linear predictors. The behavior of the maximum likelihood estimator is studied through Monte Carlo experiments. Illustrations are presented with real and simulated data.
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Tesis (Doctor en Estadística)--Pontificia Universidad Católica de Chile, 2019